Propositionalization Task

A Propositionalization Task is a data transformation task that transforms a relational dataset into a propositional dataset.

• Example(s):
  • feature creation for relational data.
  • …
• Counter-Example(s):
  • a Feature Selection Task.
• See: Feature Construction; Inductive Logic Programming; Learning from Structured Data; Multi-Instance Learning; Relational Learning; Statistical Relational Learning; Data Preparation; Propositional Logic.

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References

2011

• (Lachiche, 2011) ⇒ Nicolas Lachiche. (2011). “Propositionalization.” In: (Sammut & Webb, 2011) p.812
  • QUOTE: Propositionalization is the process of explicitly transforming a Relational dataset into a propositional dataset.

    The input data consists of examples represented by structured terms (cf. Learning from Structured Data), several predicates in First-Order Logic, or several tables in a relational database. We jointly refer to these as relational representations. The output is an Attribute-value representation in a single table, where each example corresponds to one row and is described by its values for a fixed set of attributes. New attributes are often called features to emphasize that they are built from the original attributes. The aim of propositionalization is to pre-process relational data for subsequent analysis by attribute-value learners. There are several reasons for doing this, the most important of which are: to reduce the complexity and speed up the learning; to separate modeling the data from hypothesis construction; or to use familiar attribute-value …

2003

• (Perlich & Provost, 2003) ⇒ Claudia Perlich, and Foster Provost. (2003). “Aggregation-based Feature Invention and Relational Concept Classes.” In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge discovery and data mining. ISBN:1-58113-737-0 doi:10.1145/956750.956772

2001

• (Kramer et al., 2001) ⇒ Stefan Kramer, Nada Lavrač, and Peter Flach. (2001). “Propositionalization Approaches to Relational Data Mining.” In: Relational Data Mining. ISBN:3-540-42289-7
  • QUOTE: After reviewing both general-purpose and domain-dependent propositionalization approaches from the literature, an extension to the LINUS propositionalization method that overcomes the system's earlier inability to deal with non-determinate local variables is described.

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